Search

This paper considers electric and automated buses required to follow a given line and respect a given timetable in an inter-city road. The main goal of this work is to design a control scheme in order to optimally decide, in real time, the speed profile of the bus along the line, as well as the dwell and charging times at stops. This must be done by accounting for the traffic conditions encountered in the road and by jointly minimizing the deviations from the timetable and the lack of energy in the bus battery compared with a desired level. For the resulting multi-objective optimal control problem a Pareto front analysis is performed in the paper, also considering a real test case. Relying on the analysis outcomes, an event-based control scheme is proposed, which allows, every time a bus reaches a stop, to find the most suitable Pareto-optimal solution depending on a set of state and scenario conditions referred to the expected departure time at stops, the predicted traffic conditions in the road and the state of charge of the bus battery. The performance of the proposed control scheme is tested on a real case study, thoroughly discussed in the paper.

In this paper, a two-stage modeling framework is proposed to represent the route choices and dynamics of two classes of traffic flows, namely passengers and freight, in a large-scale intermodal transportation network. More in details, the transport modes considered in this model are road transport, represented through a highway network, and rail transport. The methodology adopted in this work relies on a multi-class intermodal assignment model combined with a multi-class intermodal dynamic model. The proposed modeling scheme can be adopted for different purposes, such as to support decision makers who intend to utilize the full mobility capacity of a geographical area by allocating the mobility demand on different modes or by suggesting intermodal itineraries also when the network is affected by disruptive events. The proposed framework has been tested on a benchmark network with the aim of showing the mutual relations that occur between different modes of transport in case a connection of the intermodal network fails. Specifically, the analysis reported in the paper shows that the collapse of a railway arc can cause a high increase in travel times for some highway arcs, which result 30-40% higher than in the pre-disruption scenario, and an even greater growth in the average occupancy of many arcs.

This paper analyzes the day-to-day adjustment process of users’ behaviors in a transport network which is affected by relevant alterations such as disruptions due to critical events which cause the impossibility to use one or more links. For representing the progressive adjustment of the flows on the network to reach a new equilibrium, a day-to-day discrete-time model is proposed, based on the idea that people are bounded rational in their choices, i.e. they often do not behave according to the optimal solution but they accept solutions they consider satisfying. Users, in their choice process, are influenced by the topological similarity between the route they are currently using and others. This means that they tend to prefer the solutions that are more similar to the one they are already using. In parallel, users exhibit a myopic behavior, i.e., they tend to overestimate the goodness of a route if, when using it, they suddenly experience a significant reduction in travel time compared to what they are used to. In the paper it is shown that such route choice behaviour implies that the steady state of the system corresponds to a Bounded Rational User Equilibrium, i.e., a state that does not diverge from the user equilibrium more than a certain value which increases when the relative importance given to the topological similarity grows. The model also assumes that these biases vanish, at least with respect to those routes that are most frequently used by users, after a sufficient amount of time. Under certain conditions, it is then shown that the steady state can eventually collapse into a User Equilibrium. The effectiveness of the proposed model is assessed via simulation results in which two test networks are analyzed in detail to show the evolution of the users’ behaviour in a transport network after a disruption.